Capsule Graph Neural Network

By Zhang Xinyi, Lihu Chen. 2018

240 citations recorded by the source corpus.

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Citations in the corpus

Unsupervised Inductive Whole-Graph Embedding by Preserving Graph Proximity (2019, methodology): “…SET2SET (Gilmer et al., 2017), GRAPHSAGE, DGCNN/SORTPOOL (Zhang et al., 2018), SAN (Zhao et al., 2018), DIFFPOOL (Ying et al., 2018), CAPSGNN (Zhang & Chen, 2019), etc. Notice that most of these models adopt the neighbor aggregation based node embedding methods described previously, which…”
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity (2019, neutral): “It is natural to raise the question: Can we embed an entire graph into a vector in an unsupervised way, and how? However, most existing methods for graph-level embeddings assume a supervised model [Ying et al., 2018; Zhang and Chen, 2019], with only a few exceptions, such as GRAPH KERNELS [Yanardag ”
Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning (2021, background): “By leveraging the learning capability of neural networks, GNN achieves state-of-the-art (SOTA) performance across various domains such as per-node regression tasks, graph clustering tasks, language modelling, and control problems [32, 51, 36, 55].”
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks (2020, background): “Many GNN’s architectures with high predictive performance should be mentioned are ChebNets [4], Graph Convolutional Networks [5], GraphSage [6], Graph Attention Networks [7], among others [8, 9, 10, 11, 12, 13].”
Benchmarking Graph Neural Networks (2023, background): “Most published papers have been focused on quite small datasets like CORA and TU datasets (Kipf & Welling, 2017; Ying et al., 2018; Veličković et al., 2018; Xinyi & Chen, 2019; Xu et al., 2019; Lee et al., 2019), where all GNNs perform almost statistically the same.”